Overview

Dataset statistics

Number of variables14
Number of observations506
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.5 KiB
Average record size in memory112.3 B

Variable types

Numeric13
Categorical1

Alerts

CRIM is highly correlated with ZN and 8 other fieldsHigh correlation
ZN is highly correlated with CRIM and 4 other fieldsHigh correlation
INDUS is highly correlated with CRIM and 7 other fieldsHigh correlation
NOX is highly correlated with CRIM and 8 other fieldsHigh correlation
RM is highly correlated with LSTAT and 1 other fieldsHigh correlation
AGE is highly correlated with CRIM and 7 other fieldsHigh correlation
DIS is highly correlated with CRIM and 6 other fieldsHigh correlation
RAD is highly correlated with CRIM and 2 other fieldsHigh correlation
TAX is highly correlated with CRIM and 7 other fieldsHigh correlation
PTRATIO is highly correlated with MEDVHigh correlation
LSTAT is highly correlated with CRIM and 7 other fieldsHigh correlation
MEDV is highly correlated with CRIM and 7 other fieldsHigh correlation
CRIM is highly correlated with RAD and 1 other fieldsHigh correlation
ZN is highly correlated with INDUS and 3 other fieldsHigh correlation
INDUS is highly correlated with ZN and 6 other fieldsHigh correlation
NOX is highly correlated with ZN and 6 other fieldsHigh correlation
RM is highly correlated with LSTAT and 1 other fieldsHigh correlation
AGE is highly correlated with ZN and 5 other fieldsHigh correlation
DIS is highly correlated with ZN and 4 other fieldsHigh correlation
RAD is highly correlated with CRIM and 3 other fieldsHigh correlation
TAX is highly correlated with CRIM and 6 other fieldsHigh correlation
PTRATIO is highly correlated with MEDVHigh correlation
LSTAT is highly correlated with INDUS and 5 other fieldsHigh correlation
MEDV is highly correlated with RM and 2 other fieldsHigh correlation
CRIM is highly correlated with INDUS and 4 other fieldsHigh correlation
ZN is highly correlated with INDUS and 1 other fieldsHigh correlation
INDUS is highly correlated with CRIM and 3 other fieldsHigh correlation
NOX is highly correlated with CRIM and 4 other fieldsHigh correlation
AGE is highly correlated with NOX and 1 other fieldsHigh correlation
DIS is highly correlated with CRIM and 3 other fieldsHigh correlation
RAD is highly correlated with CRIM and 1 other fieldsHigh correlation
TAX is highly correlated with CRIM and 1 other fieldsHigh correlation
LSTAT is highly correlated with MEDVHigh correlation
MEDV is highly correlated with LSTATHigh correlation
CRIM is highly correlated with INDUS and 2 other fieldsHigh correlation
ZN is highly correlated with INDUS and 7 other fieldsHigh correlation
INDUS is highly correlated with CRIM and 8 other fieldsHigh correlation
NOX is highly correlated with CRIM and 9 other fieldsHigh correlation
RM is highly correlated with PTRATIO and 3 other fieldsHigh correlation
AGE is highly correlated with ZN and 7 other fieldsHigh correlation
DIS is highly correlated with ZN and 8 other fieldsHigh correlation
RAD is highly correlated with ZN and 8 other fieldsHigh correlation
TAX is highly correlated with ZN and 5 other fieldsHigh correlation
PTRATIO is highly correlated with ZN and 9 other fieldsHigh correlation
B is highly correlated with CRIM and 1 other fieldsHigh correlation
LSTAT is highly correlated with NOX and 6 other fieldsHigh correlation
MEDV is highly correlated with ZN and 8 other fieldsHigh correlation
ZN has 372 (73.5%) zeros Zeros

Reproduction

Analysis started2022-05-08 07:30:33.554994
Analysis finished2022-05-08 07:31:10.793686
Duration37.24 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

CRIM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct504
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.613523557
Minimum0.00632
Maximum88.9762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:11.052277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.02791
Q10.082045
median0.25651
Q33.6770825
95-th percentile15.78915
Maximum88.9762
Range88.96988
Interquartile range (IQR)3.5950375

Descriptive statistics

Standard deviation8.601545105
Coefficient of variation (CV)2.380376098
Kurtosis37.13050913
Mean3.613523557
Median Absolute Deviation (MAD)0.22145
Skewness5.223148798
Sum1828.44292
Variance73.9865782
MonotonicityNot monotonic
2022-05-08T13:01:11.326836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.015012
 
0.4%
14.33372
 
0.4%
0.034661
 
0.2%
0.031131
 
0.2%
0.030491
 
0.2%
0.025431
 
0.2%
0.024981
 
0.2%
0.013011
 
0.2%
0.061511
 
0.2%
0.054971
 
0.2%
Other values (494)494
97.6%
ValueCountFrequency (%)
0.006321
0.2%
0.009061
0.2%
0.010961
0.2%
0.013011
0.2%
0.013111
0.2%
0.01361
0.2%
0.013811
0.2%
0.014321
0.2%
0.014391
0.2%
0.015012
0.4%
ValueCountFrequency (%)
88.97621
0.2%
73.53411
0.2%
67.92081
0.2%
51.13581
0.2%
45.74611
0.2%
41.52921
0.2%
38.35181
0.2%
37.66191
0.2%
28.65581
0.2%
25.94061
0.2%

ZN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.36363636
Minimum0
Maximum100
Zeros372
Zeros (%)73.5%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:11.630076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.32245299
Coefficient of variation (CV)2.052375864
Kurtosis4.031510084
Mean11.36363636
Median Absolute Deviation (MAD)0
Skewness2.225666323
Sum5750
Variance543.9368137
MonotonicityNot monotonic
2022-05-08T13:01:11.942595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0372
73.5%
2021
 
4.2%
8015
 
3.0%
2210
 
2.0%
12.510
 
2.0%
2510
 
2.0%
407
 
1.4%
456
 
1.2%
306
 
1.2%
905
 
1.0%
Other values (16)44
 
8.7%
ValueCountFrequency (%)
0372
73.5%
12.510
 
2.0%
17.51
 
0.2%
181
 
0.2%
2021
 
4.2%
214
 
0.8%
2210
 
2.0%
2510
 
2.0%
283
 
0.6%
306
 
1.2%
ValueCountFrequency (%)
1001
 
0.2%
954
 
0.8%
905
 
1.0%
852
 
0.4%
82.52
 
0.4%
8015
3.0%
753
 
0.6%
703
 
0.6%
604
 
0.8%
553
 
0.6%

INDUS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct76
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.13677866
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:12.268836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.89
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.860352941
Coefficient of variation (CV)0.6160087358
Kurtosis-1.233539601
Mean11.13677866
Median Absolute Deviation (MAD)6.32
Skewness0.2950215679
Sum5635.21
Variance47.06444247
MonotonicityNot monotonic
2022-05-08T13:01:12.718256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1132
26.1%
19.5830
 
5.9%
8.1422
 
4.3%
6.218
 
3.6%
21.8915
 
3.0%
3.9712
 
2.4%
9.912
 
2.4%
8.5611
 
2.2%
10.5911
 
2.2%
5.8610
 
2.0%
Other values (66)233
46.0%
ValueCountFrequency (%)
0.461
 
0.2%
0.741
 
0.2%
1.211
 
0.2%
1.221
 
0.2%
1.252
0.4%
1.321
 
0.2%
1.381
 
0.2%
1.472
0.4%
1.524
0.8%
1.692
0.4%
ValueCountFrequency (%)
27.745
 
1.0%
25.657
 
1.4%
21.8915
 
3.0%
19.5830
 
5.9%
18.1132
26.1%
15.043
 
0.6%
13.925
 
1.0%
13.894
 
0.8%
12.836
 
1.2%
11.935
 
1.0%

CHAS
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
0
471 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters506
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Length

2022-05-08T13:01:12.969581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-08T13:01:13.274591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number506
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common506
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

NOX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5546950593
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:13.549955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.40925
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.1158776757
Coefficient of variation (CV)0.2089033853
Kurtosis-0.06466713337
Mean0.5546950593
Median Absolute Deviation (MAD)0.0875
Skewness0.7293079225
Sum280.6757
Variance0.01342763572
MonotonicityNot monotonic
2022-05-08T13:01:13.876134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53823
 
4.5%
0.71318
 
3.6%
0.43717
 
3.4%
0.87116
 
3.2%
0.62415
 
3.0%
0.48915
 
3.0%
0.69314
 
2.8%
0.60514
 
2.8%
0.7413
 
2.6%
0.54412
 
2.4%
Other values (71)349
69.0%
ValueCountFrequency (%)
0.3851
 
0.2%
0.3891
 
0.2%
0.3922
0.4%
0.3941
 
0.2%
0.3982
0.4%
0.44
0.8%
0.4013
0.6%
0.4033
0.6%
0.4043
0.6%
0.4053
0.6%
ValueCountFrequency (%)
0.87116
3.2%
0.778
1.6%
0.7413
2.6%
0.7186
 
1.2%
0.71318
3.6%
0.711
2.2%
0.69314
2.8%
0.6798
1.6%
0.6717
 
1.4%
0.6683
 
0.6%

RM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct446
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.284634387
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:14.208949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.314
Q15.8855
median6.2085
Q36.6235
95-th percentile7.5875
Maximum8.78
Range5.219
Interquartile range (IQR)0.738

Descriptive statistics

Standard deviation0.7026171434
Coefficient of variation (CV)0.1117992074
Kurtosis1.891500366
Mean6.284634387
Median Absolute Deviation (MAD)0.3455
Skewness0.4036121333
Sum3180.025
Variance0.4936708502
MonotonicityNot monotonic
2022-05-08T13:01:14.491284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.7133
 
0.6%
6.1673
 
0.6%
6.1273
 
0.6%
6.2293
 
0.6%
6.4053
 
0.6%
6.4173
 
0.6%
6.7822
 
0.4%
6.9512
 
0.4%
6.632
 
0.4%
6.3122
 
0.4%
Other values (436)480
94.9%
ValueCountFrequency (%)
3.5611
0.2%
3.8631
0.2%
4.1382
0.4%
4.3681
0.2%
4.5191
0.2%
4.6281
0.2%
4.6521
0.2%
4.881
0.2%
4.9031
0.2%
4.9061
0.2%
ValueCountFrequency (%)
8.781
0.2%
8.7251
0.2%
8.7041
0.2%
8.3981
0.2%
8.3751
0.2%
8.3371
0.2%
8.2971
0.2%
8.2661
0.2%
8.2591
0.2%
8.2471
0.2%

AGE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct356
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.57490119
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:14.801473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.725
Q145.025
median77.5
Q394.075
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)49.05

Descriptive statistics

Standard deviation28.14886141
Coefficient of variation (CV)0.410483441
Kurtosis-0.9677155942
Mean68.57490119
Median Absolute Deviation (MAD)19.55
Skewness-0.5989626399
Sum34698.9
Variance792.3583985
MonotonicityNot monotonic
2022-05-08T13:01:15.132744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10043
 
8.5%
95.44
 
0.8%
964
 
0.8%
98.24
 
0.8%
97.94
 
0.8%
98.84
 
0.8%
87.94
 
0.8%
95.63
 
0.6%
973
 
0.6%
21.43
 
0.6%
Other values (346)430
85.0%
ValueCountFrequency (%)
2.91
0.2%
61
0.2%
6.21
0.2%
6.51
0.2%
6.62
0.4%
6.81
0.2%
7.82
0.4%
8.41
0.2%
8.91
0.2%
9.81
0.2%
ValueCountFrequency (%)
10043
8.5%
99.31
 
0.2%
99.11
 
0.2%
98.93
 
0.6%
98.84
 
0.8%
98.71
 
0.2%
98.51
 
0.2%
98.42
 
0.4%
98.32
 
0.4%
98.24
 
0.8%

DIS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct412
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.795042688
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:15.410141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.461975
Q12.100175
median3.20745
Q35.188425
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.08825

Descriptive statistics

Standard deviation2.105710127
Coefficient of variation (CV)0.5548580872
Kurtosis0.4879411222
Mean3.795042688
Median Absolute Deviation (MAD)1.29115
Skewness1.011780579
Sum1920.2916
Variance4.434015137
MonotonicityNot monotonic
2022-05-08T13:01:15.644780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.49525
 
1.0%
5.72094
 
0.8%
5.28734
 
0.8%
6.81474
 
0.8%
5.40074
 
0.8%
6.33613
 
0.6%
3.94543
 
0.6%
6.4983
 
0.6%
4.72113
 
0.6%
4.81223
 
0.6%
Other values (402)470
92.9%
ValueCountFrequency (%)
1.12961
0.2%
1.1371
0.2%
1.16911
0.2%
1.17421
0.2%
1.17811
0.2%
1.20241
0.2%
1.28521
0.2%
1.31631
0.2%
1.32161
0.2%
1.33251
0.2%
ValueCountFrequency (%)
12.12651
0.2%
10.71032
0.4%
10.58572
0.4%
9.22291
0.2%
9.22032
0.4%
9.18761
0.2%
9.08921
0.2%
8.90672
0.4%
8.79212
0.4%
8.69661
0.2%

RAD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.549407115
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:15.894505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.707259384
Coefficient of variation (CV)0.9118115166
Kurtosis-0.8672319936
Mean9.549407115
Median Absolute Deviation (MAD)2
Skewness1.004814648
Sum4832
Variance75.81636598
MonotonicityNot monotonic
2022-05-08T13:01:16.158562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24132
26.1%
5115
22.7%
4110
21.7%
338
 
7.5%
626
 
5.1%
224
 
4.7%
824
 
4.7%
120
 
4.0%
717
 
3.4%
ValueCountFrequency (%)
120
 
4.0%
224
 
4.7%
338
 
7.5%
4110
21.7%
5115
22.7%
626
 
5.1%
717
 
3.4%
824
 
4.7%
24132
26.1%
ValueCountFrequency (%)
24132
26.1%
824
 
4.7%
717
 
3.4%
626
 
5.1%
5115
22.7%
4110
21.7%
338
 
7.5%
224
 
4.7%
120
 
4.0%

TAX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct66
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.2371542
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:16.443020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.5371161
Coefficient of variation (CV)0.4128411987
Kurtosis-1.142407992
Mean408.2371542
Median Absolute Deviation (MAD)73
Skewness0.6699559418
Sum206568
Variance28404.75949
MonotonicityNot monotonic
2022-05-08T13:01:16.917671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666132
26.1%
30740
 
7.9%
40330
 
5.9%
43715
 
3.0%
30414
 
2.8%
26412
 
2.4%
39812
 
2.4%
38411
 
2.2%
27711
 
2.2%
22410
 
2.0%
Other values (56)219
43.3%
ValueCountFrequency (%)
1871
 
0.2%
1887
1.4%
1938
1.6%
1981
 
0.2%
2165
1.0%
2227
1.4%
2235
1.0%
22410
2.0%
2261
 
0.2%
2339
1.8%
ValueCountFrequency (%)
7115
 
1.0%
666132
26.1%
4691
 
0.2%
43715
 
3.0%
4329
 
1.8%
4303
 
0.6%
4221
 
0.2%
4112
 
0.4%
40330
 
5.9%
4022
 
0.4%

PTRATIO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.4555336
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:17.190986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.05
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.164945524
Coefficient of variation (CV)0.1173060379
Kurtosis-0.2850913833
Mean18.4555336
Median Absolute Deviation (MAD)1.15
Skewness-0.8023249269
Sum9338.5
Variance4.686989121
MonotonicityNot monotonic
2022-05-08T13:01:17.507548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.2140
27.7%
14.734
 
6.7%
2127
 
5.3%
17.823
 
4.5%
19.219
 
3.8%
17.418
 
3.6%
18.617
 
3.4%
19.117
 
3.4%
18.416
 
3.2%
16.616
 
3.2%
Other values (36)179
35.4%
ValueCountFrequency (%)
12.63
 
0.6%
1312
 
2.4%
13.61
 
0.2%
14.41
 
0.2%
14.734
6.7%
14.83
 
0.6%
14.94
 
0.8%
15.11
 
0.2%
15.213
 
2.6%
15.33
 
0.6%
ValueCountFrequency (%)
222
 
0.4%
21.215
 
3.0%
21.11
 
0.2%
2127
 
5.3%
20.911
 
2.2%
20.2140
27.7%
20.15
 
1.0%
19.78
 
1.6%
19.68
 
1.6%
19.219
 
3.8%

B
Real number (ℝ≥0)

HIGH CORRELATION

Distinct357
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.6740316
Minimum0.32
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:17.850785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile84.59
Q1375.3775
median391.44
Q3396.225
95-th percentile396.9
Maximum396.9
Range396.58
Interquartile range (IQR)20.8475

Descriptive statistics

Standard deviation91.29486438
Coefficient of variation (CV)0.255961624
Kurtosis7.226817549
Mean356.6740316
Median Absolute Deviation (MAD)5.46
Skewness-2.890373712
Sum180477.06
Variance8334.752263
MonotonicityNot monotonic
2022-05-08T13:01:18.158097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9121
 
23.9%
393.743
 
0.6%
395.243
 
0.6%
376.142
 
0.4%
394.722
 
0.4%
395.632
 
0.4%
392.82
 
0.4%
395.562
 
0.4%
390.942
 
0.4%
393.682
 
0.4%
Other values (347)365
72.1%
ValueCountFrequency (%)
0.321
0.2%
2.521
0.2%
2.61
0.2%
3.51
0.2%
3.651
0.2%
6.681
0.2%
7.681
0.2%
9.321
0.2%
10.481
0.2%
16.451
0.2%
ValueCountFrequency (%)
396.9121
23.9%
396.421
 
0.2%
396.331
 
0.2%
396.31
 
0.2%
396.281
 
0.2%
396.241
 
0.2%
396.231
 
0.2%
396.212
 
0.4%
396.141
 
0.2%
396.062
 
0.4%

LSTAT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct455
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.65306324
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:18.416893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.7075
Q16.95
median11.36
Q316.955
95-th percentile26.8075
Maximum37.97
Range36.24
Interquartile range (IQR)10.005

Descriptive statistics

Standard deviation7.141061511
Coefficient of variation (CV)0.5643741263
Kurtosis0.4932395174
Mean12.65306324
Median Absolute Deviation (MAD)4.795
Skewness0.9064600936
Sum6402.45
Variance50.99475951
MonotonicityNot monotonic
2022-05-08T13:01:18.699099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.793
 
0.6%
14.13
 
0.6%
6.363
 
0.6%
18.133
 
0.6%
8.053
 
0.6%
5.292
 
0.4%
13.442
 
0.4%
7.442
 
0.4%
18.062
 
0.4%
5.492
 
0.4%
Other values (445)481
95.1%
ValueCountFrequency (%)
1.731
0.2%
1.921
0.2%
1.981
0.2%
2.471
0.2%
2.871
0.2%
2.881
0.2%
2.941
0.2%
2.961
0.2%
2.971
0.2%
2.981
0.2%
ValueCountFrequency (%)
37.971
0.2%
36.981
0.2%
34.771
0.2%
34.411
0.2%
34.371
0.2%
34.021
0.2%
31.991
0.2%
30.812
0.4%
30.631
0.2%
30.621
0.2%

MEDV
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct229
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.53280632
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-05-08T13:01:19.041788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117.025
median21.2
Q325
95-th percentile43.4
Maximum50
Range45
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation9.197104087
Coefficient of variation (CV)0.408165053
Kurtosis1.495196944
Mean22.53280632
Median Absolute Deviation (MAD)4
Skewness1.108098408
Sum11401.6
Variance84.58672359
MonotonicityNot monotonic
2022-05-08T13:01:19.365887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5016
 
3.2%
258
 
1.6%
227
 
1.4%
21.77
 
1.4%
23.17
 
1.4%
19.46
 
1.2%
20.66
 
1.2%
13.85
 
1.0%
21.45
 
1.0%
20.15
 
1.0%
Other values (219)434
85.8%
ValueCountFrequency (%)
52
0.4%
5.61
 
0.2%
6.31
 
0.2%
72
0.4%
7.23
0.6%
7.41
 
0.2%
7.51
 
0.2%
8.11
 
0.2%
8.32
0.4%
8.42
0.4%
ValueCountFrequency (%)
5016
3.2%
48.81
 
0.2%
48.51
 
0.2%
48.31
 
0.2%
46.71
 
0.2%
461
 
0.2%
45.41
 
0.2%
44.81
 
0.2%
441
 
0.2%
43.81
 
0.2%

Interactions

2022-05-08T13:01:07.731763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:37.521676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:39.779379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:41.916770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:44.175149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:46.397754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:48.616660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:50.936235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:53.203929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:55.472309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:58.630507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:01.556747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:04.859553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:07.889977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:37.734252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:39.927888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:42.059029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:44.335565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:46.563349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:48.780259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:51.110542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:53.337017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:55.652191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:58.905586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:01.805298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:05.049860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:08.056791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:37.920059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:40.059023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:42.223609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:44.491617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:46.738397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:48.958881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:51.291859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:53.471364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:55.840453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:59.162448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:02.053101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:05.244514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:08.220897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:38.053077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:40.243418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:42.489223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:44.667609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:46.937052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:49.112453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:51.501907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:53.692506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:56.047153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:59.401058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:02.277467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:05.529425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:08.518779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:38.224829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:40.402096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:42.646889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:44.820033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:47.128792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:49.289192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:51.675720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:53.956332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:56.255157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:59.610498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:02.473104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:05.892951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:08.720273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:38.399551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:40.548280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:42.774635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:44.969934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:47.295125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:49.423296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:51.826438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:54.093837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:56.452250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:59.782870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:02.700295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:06.096671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:08.899935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:38.548608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:40.723012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:42.969911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:45.155951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:47.425462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:49.558144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:51.993886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:54.288024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:56.778177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:59.984910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:02.850623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:06.300611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:09.042084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:38.808341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:40.891036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:43.141241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:45.289491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:47.566949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:49.782972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:52.183644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:54.462097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:56.995212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:00.175471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:03.022393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:06.497072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:09.198824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:38.970937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:41.080656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:43.323450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:45.433948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:47.753760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:50.062302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:52.312559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:54.619051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:57.221525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:00.334754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:03.308836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:06.700796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:09.366359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:39.136120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:41.265635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:43.487113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:45.603604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:47.939047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:50.264294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:52.474036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:54.767445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:57.417460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:00.568645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:03.740450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:06.878665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:09.536557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:39.290370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:41.433574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:43.646537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:45.796247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:48.105500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:50.446408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:52.641323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:54.909242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:57.671417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:00.807385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:03.972997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:07.112587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:09.694646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:39.468755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:41.618127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:43.805358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:45.977616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:48.284951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:50.638006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:52.802424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:55.064291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:57.923122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:01.015459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:04.324744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:07.309676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:09.867501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:39.598543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:41.781248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:43.991215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:46.267147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:48.448172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:50.787064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:52.984895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:55.310740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:00:58.186221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:01.287437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:04.652743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T13:01:07.541531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-08T13:01:19.682833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-08T13:01:19.990692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-08T13:01:20.309147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-08T13:01:20.767341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-08T13:01:10.308958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-08T13:01:10.691508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
00.0063218.02.3100.5386.57565.24.09001296.015.3396.904.9824.0
10.027310.07.0700.4696.42178.94.96712242.017.8396.909.1421.6
20.027290.07.0700.4697.18561.14.96712242.017.8392.834.0334.7
30.032370.02.1800.4586.99845.86.06223222.018.7394.632.9433.4
40.069050.02.1800.4587.14754.26.06223222.018.7396.905.3336.2
50.029850.02.1800.4586.43058.76.06223222.018.7394.125.2128.7
60.0882912.57.8700.5246.01266.65.56055311.015.2395.6012.4322.9
70.1445512.57.8700.5246.17296.15.95055311.015.2396.9019.1527.1
80.2112412.57.8700.5245.631100.06.08215311.015.2386.6329.9316.5
90.1700412.57.8700.5246.00485.96.59215311.015.2386.7117.1018.9

Last rows

CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
4960.289600.09.6900.5855.39072.92.79866391.019.2396.9021.1419.7
4970.268380.09.6900.5855.79470.62.89276391.019.2396.9014.1018.3
4980.239120.09.6900.5856.01965.32.40916391.019.2396.9012.9221.2
4990.177830.09.6900.5855.56973.52.39996391.019.2395.7715.1017.5
5000.224380.09.6900.5856.02779.72.49826391.019.2396.9014.3316.8
5010.062630.011.9300.5736.59369.12.47861273.021.0391.999.6722.4
5020.045270.011.9300.5736.12076.72.28751273.021.0396.909.0820.6
5030.060760.011.9300.5736.97691.02.16751273.021.0396.905.6423.9
5040.109590.011.9300.5736.79489.32.38891273.021.0393.456.4822.0
5050.047410.011.9300.5736.03080.82.50501273.021.0396.907.8811.9